By Alexander I. Galushkin
"Neural Networks conception is an important contribution to the neural networks literature. it's a treasure trove that are supposed to be mined by way of the millions of researchers and practitioners around the globe who've now not formerly had entry to the end result of Soviet and Russian neural community learn. Dr. Galushkin is to be congratulated and thanked for his of completion of this huge paintings; a ebook that simply he may well write. it's a significant present to the world."
Robert Hecht Nielsen, Computational Neurobiology, college of California, San Diego
"Professor Galushkin’s monograph has many targeted good points that during totality make his paintings an incredible contribution to the literature of neural networks conception. He and his writer deserve profuse thank you and congratulations from all who're heavily drawn to the rules of neural networks thought, its evolution and present status."
Lotfi Zadeh, Berkeley, founding father of Fuzziness
"Professor Galushkin, a pace-setter in neural networks concept in Russia, makes use of mathematical equipment together with complexity thought, nonlinear dynamics and optimization, suggestions which are solidly grounded in Russian culture. His thought is expansive: protecting not only the normal subject matters comparable to community structure, it additionally addresses neural continua in functionality areas. i'm happy to determine his thought offered in its entirety the following, for the 1st time for plenty of, in order that the either thought he constructed and the technique he took to appreciate such complicated phenomena could be absolutely appreciated."
Sun-Ichi Amari, Director of RIKEN mind technology Institute RIKEN
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Sample text
This computer type is similar to the type-11-computers equipped with synchronously functioning clusters of processor elements implementing neural algorithms. It is necessary to mention that computers of 4-, 8- and 12-types include a hardware/ software implementation of “neural clusters”. Such an implementation is significantly oriented namely on the solution based on the neural algorithms with corresponding elemental base, architecture, and program development systems. From our viewpoint, it is also noteworthy that at present, the computer systems of types 9, 11, and 12 are the most perspective.
Special attention here is paid to the selection of optimal parameters of the neural network tuning circuit based on the estimation of the primary optimization functional value. The results of investigations of a large number of multilayer neural networks obtained by means of computer modeling are presented in the studies [I-1, I-6]. In general, one must take into account the following aspects concerning this multilayer neural network synthesis stage. The consideration of a non-formal problem class of pattern recognition under the unknown and sufficiently complex functions of distribution densities causes difficulties not only for the process of development of such systems, but also for the attempts to estimate theoretically the quality of the solution of these problems.
Ivakhnenko AG (1975) Perceptrons – systems of pattern recognition. Kiev, Naukova Dumka 22. Galushkin AI, Kudryavtsev AM (1976) Matrix inversion with the help of a multilayer system based on the threshold elements. “Cybernetics and computer technology”. Kiev, Naukova Dumka 33 23. Agababyan KG (1976) Perception of angle size by neural structures. Cybernetics 5:173–176 24. Galushkin AI (1977) Continual models of multilayer pattern recognition systems. 2 Neural Computer Implementation 1. Poupkov KA, Narimanov VK, Galushkin AI (1971) Specialized recognition device.